1. Technical Field
The present disclosure relates generally to prediction based on event sequences, and more particularly to prediction of failure based on sequences of messages from equipment or systems.
2. Discussion of Related Art
There is a strong trend towards using predictive methods to improve maintenance of equipment such as medical scanners, gas turbines, solar plants, computer software and so forth. Proactive maintenance strategies may significantly lower costs and improve customer satisfaction. Repairs can be done via scheduled downtime, spare parts can be ordered ahead of time, and computing resources can be reallocated in advance if there is strong evidence that a particular failure will occur.
It is useful to be able to find patterns indicative of an upcoming failure or a need for intervention. The above-mentioned systems may be produce large amounts of temporal data such as sensor measurements, log messages, execution traces, etc. Currently, an analyst is expected to manually look for failure-predictive patterns by examining this temporal data.
However, it can be very difficult for a human analyst to determine whether a sequence of events, e.g., a sequence of logged messages, is a pattern, and whether that pattern is likely to result in a future failure of equipment or a system. Thus, there is a need for an analytic approach that can determine automatically from the temporal data whether a subsequent failure is likely to occur.